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Paper Citation Record · LEDGER

FBGEMM: Enabling High-Performance Low-Precision Deep Learning Inference

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 16 inbound Pith citation observations for arXiv:2101.05615.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2101.05615 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 16 of 16 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 16 of 16 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T23:36:10.063012Z

measured 1 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

20
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation f5dbf02c-671b-4976-ba35-6f697b754af9 · inbound

LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale cites this paper.

LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale FBGEMM: Enabling High-Performance Low-Precision Deep Learning Inference

Reference 142

Resolution
verified exact
arxiv_id, observed 2026-05-13T13:35:36.105091Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-05-13T13:35:35.972596Z digest=sha256:b9f431370447a84102f866ce400a160f09c06ef1a17c482bee8a8d819e78894d

Observation 2760bea2-f8f3-4305-86a7-8cd51bcb7228 · inbound

Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendations cites this paper.

Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendations FBGEMM: Enabling High-Performance Low-Precision Deep Learning Inference

Reference 124

Resolution
verified exact
arxiv_id, observed 2026-05-13T19:39:32.859337Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-05-13T19:39:32.740496Z digest=sha256:39f48a913001b92e466b8d1f2a76b167a0c8fb1631e54d2aa2f4d8588aa559bc

Observation 96eee25f-712e-4760-b1c1-27ccf601c395 · inbound

FEATHER: A Reconfigurable Accelerator with Data Reordering Support for Low-Cost On-Chip Dataflow Switching cites this paper.

FEATHER: A Reconfigurable Accelerator with Data Reordering Support for Low-Cost On-Chip Dataflow Switching FBGEMM: Enabling High-Performance Low-Precision Deep Learning Inference

Reference 32

Resolution
verified exact
arxiv_id, observed 2026-05-24T01:13:42.916362Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-05-24T01:09:38.836949Z digest=sha256:b3c3046fdf62f1e5355ac4779519efee5e71a70e245043870ded7286a978f9d3

Observation d3266c7c-d358-4813-83ac-b72e9fd23524 · inbound

FluidML: Fast and Memory Efficient Inference Optimization cites this paper.

FluidML: Fast and Memory Efficient Inference Optimization FBGEMM: Enabling High-Performance Low-Precision Deep Learning Inference

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-12T20:56:15.466664Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T20:56:15.466664Z digest=sha256:64776a6020c086eb6826349fda952760374cdd2a358c67a3ee5936971fe0b073

Observation 5b033ee9-601f-4d05-bac6-400ec649f6a7 · inbound

$\mu$nit Scaling: Simple and Scalable FP8 LLM Training cites this paper.

$\mu$nit Scaling: Simple and Scalable FP8 LLM Training FBGEMM: Enabling High-Performance Low-Precision Deep Learning Inference

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-08T17:19:29.168874Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T17:19:29.168874Z digest=sha256:020ac6af2af9e65d3976eb4bb7a3e9b15bcb73a8e205449e94903b724fbba38a

Observation 0f717338-31bc-4514-a363-dc7e6ffcba63 · inbound

Deep Learning Innovations for Energy Efficiency: Advances in Non-Intrusive Load Monitoring and EV Charging Optimization for a Sustainable Grid cites this paper.

Deep Learning Innovations for Energy Efficiency: Advances in Non-Intrusive Load Monitoring and EV Charging Optimization for a Sustainable Grid FBGEMM: Enabling High-Performance Low-Precision Deep Learning Inference

Reference 100

Resolution
unresolved
no resolver link, observed 2026-08-15T23:36:10.063012Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T23:36:10.063012Z digest=sha256:b715496912c8e8fa405ffe4cd05e55aebbcd475df9b0e7896342a2e29513b8bc

Observation 07c1218e-4f7c-43b9-a57c-2a30ab3652fb · inbound

PinFM: Foundation Model for User Activity Sequences at a Billion-scale Visual Discovery Platform cites this paper.

PinFM: Foundation Model for User Activity Sequences at a Billion-scale Visual Discovery Platform FBGEMM: Enabling High-Performance Low-Precision Deep Learning Inference

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-06T16:47:31.893525Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:47:31.893525Z digest=sha256:61ba5e57d8eb4e164364ccbe7f6623df19390d71ddb70ccdcd99344b3ce26899

Observation dd22d635-801e-4206-9aa6-fcea97909e6a · inbound

Towards Automated Kernel Generation in the Era of LLMs cites this paper.

Towards Automated Kernel Generation in the Era of LLMs FBGEMM: Enabling High-Performance Low-Precision Deep Learning Inference

Reference 2020

Resolution
unresolved
no resolver link, observed 2026-08-03T08:49:48.482023Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T08:49:48.482023Z digest=sha256:e26b813c0831496874a8ceb0001fe262075e7278db24ea58852666e8acf4c504

Observation aa3f774a-d437-4994-b5e1-5a13dd7bee76 · inbound

Privatar: Scalable Privacy-preserving Multi-user VR via Secure Offloading cites this paper.

Privatar: Scalable Privacy-preserving Multi-user VR via Secure Offloading FBGEMM: Enabling High-Performance Low-Precision Deep Learning Inference

Reference 160

Resolution
metadata mismatch
arxiv_id, observed 2026-05-10T06:21:26.896267Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-05-10T06:20:18.479234Z digest=sha256:aa6bc2c7534c35aca304fb13bd77c86c82cdc8df5de7f07da9664eabdf54536e

Observation b5c8fb0b-47ee-4488-a785-69b66c574855 · inbound

At the Edge of the Heart: ULP FPGA-Based CNN for On-Device Cardiac Feature Extraction in Smart Health Sensors for Astronauts cites this paper.

At the Edge of the Heart: ULP FPGA-Based CNN for On-Device Cardiac Feature Extraction in Smart Health Sensors for Astronauts FBGEMM: Enabling High-Performance Low-Precision Deep Learning Inference

Reference 17

Resolution
verified exact
arxiv_id, observed 2026-05-12T00:46:12.479046Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-05-07T14:26:58.326728Z digest=sha256:dbefc99632d4fa926477bf1836eee54f8c0922862382d46bee630bc739de1fec

Observation 95920229-3736-482e-80cc-1ff5087bff86 · inbound

One Pool, Two Caches: Adaptive HBM Partitioning for Accelerating Generative Recommender Serving cites this paper.

One Pool, Two Caches: Adaptive HBM Partitioning for Accelerating Generative Recommender Serving FBGEMM: Enabling High-Performance Low-Precision Deep Learning Inference

Reference 23

Resolution
verified exact
arxiv_id, observed 2026-05-08T19:49:07.534023Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-05-08T17:44:18.414311Z digest=sha256:74130f5f2dfdbbef2b40e58b0410cb1a4fca2b8e6691f140c6f794d99149ebde

Observation 1292add8-cc9f-451e-9259-7b48553ee9a5 · inbound

An Efficient Hybrid Sparse Attention with CPU-GPU Parallelism for Long-Context Inference cites this paper.

An Efficient Hybrid Sparse Attention with CPU-GPU Parallelism for Long-Context Inference FBGEMM: Enabling High-Performance Low-Precision Deep Learning Inference

Reference 24

Resolution
verified exact
arxiv_id, observed 2026-05-11T03:05:54.157523Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-05-11T02:56:28.828593Z digest=sha256:af8d950c4ca32dd4d93745a58faa8bd11bb167fcc5521e70aecf644c00dafc6a

Observation dfa390ab-a690-4d8a-af32-b1c62461146b · inbound

LoKA: Low-precision Kernel Applications for Recommendation Models At Scale cites this paper.

LoKA: Low-precision Kernel Applications for Recommendation Models At Scale FBGEMM: Enabling High-Performance Low-Precision Deep Learning Inference

Reference 43

Resolution
verified exact
arxiv_id, observed 2026-05-12T06:06:28.175681Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-05-12T04:33:41.411292Z digest=sha256:6de417ccac8f79bfc387f9f1dd824b61ab699b4acc80ac63d8bc84981372b3aa

Observation 47b5be0e-cc8a-43b0-8c48-04cc7de10e60 · inbound

LoKA: Low-precision Kernel Applications for Recommendation Models At Scale cites this paper.

LoKA: Low-precision Kernel Applications for Recommendation Models At Scale FBGEMM: Enabling High-Performance Low-Precision Deep Learning Inference

Reference 43

Resolution
verified exact
arxiv_id, observed 2026-05-15T04:59:46.240687Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-05-15T04:55:01.973832Z digest=sha256:bdad6b72fafcc81d7222185abf48b340621427779abdb954be95896bea62d416

Observation 5c7115d2-098a-4304-a357-d34a96f3594c · inbound

DPIFrame: A Dual-Level Parallelism Acceleration Framework for CTR Model Inference cites this paper.

DPIFrame: A Dual-Level Parallelism Acceleration Framework for CTR Model Inference FBGEMM: Enabling High-Performance Low-Precision Deep Learning Inference

Reference 43

Resolution
verified exact
arxiv_id, observed 2026-07-04T07:09:38.142853Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-06-26T13:42:59.458894Z digest=sha256:336d06b031385f9a274cce34f9f870abf78927e423d852c32d2246366ad0a5e1

Observation 07555963-89e6-40ca-b723-6a9d7d261074 · inbound

Energy-Efficient CNN Acceleration with MSDF Digit-Serial Arithmetic on FPGA cites this paper.

Energy-Efficient CNN Acceleration with MSDF Digit-Serial Arithmetic on FPGA FBGEMM: Enabling High-Performance Low-Precision Deep Learning Inference

Reference 15

Resolution
verified exact
arxiv_id, observed 2026-07-04T20:50:10.678976Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-06-25T19:39:19.171282Z digest=sha256:4eb14285a56707a96b634684b35d086329dc901caaa6a0d1477df53a66960a7f